Isolated Handwritten Arabic Character Recognition Using Convolutional Neural Networks: An Overview

Arabic Handwriting Recognition (AHR) is a research area of great importance, given the intricacies of Arabic script. The recognition of Isolated Handwritten Arabic Characters (IHAC) is a crucial phase in AHR, and significant progress has been made in recent years, primarily due to the adoption of Convolutional Neural Networks (CNN). CNN has emerged as a powerful learning technique, dominating various computer vision-related research domains. Notably, CNNs have been extensively utilized for IHAC recognition since 2017. This paper presents an analysis of CNN-based methods employed in IHAC recognition. We delve into the advancements made in network architectures, training strategies, datasets, and results. The findings from this review emphasize the immense potential of CNN-based methods in IHAC recognition and shed light on future research directions to tackle the challenges associated with this field. Overall, CNN-based methods hold promising prospects for improving the accuracy and efficiency of IHAC recognition, which can have far-reaching applications in document analysis, text recognition, and language processing.